Overview

Dataset statistics

Number of variables9
Number of observations1030
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)1.1%
Total size in memory72.6 KiB
Average record size in memory72.1 B

Variable types

Numeric9

Alerts

Dataset has 11 (1.1%) duplicate rowsDuplicates
Age is highly overall correlated with StrengthHigh correlation
Strength is highly overall correlated with AgeHigh correlation
Superplasticizer is highly overall correlated with WaterHigh correlation
Water is highly overall correlated with SuperplasticizerHigh correlation
Blast Furnace Slag has 471 (45.7%) zerosZeros
Fly Ash has 566 (55.0%) zerosZeros
Superplasticizer has 379 (36.8%) zerosZeros

Reproduction

Analysis started2025-12-30 19:36:22.260370
Analysis finished2025-12-30 19:36:30.142521
Duration7.88 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Cement
Real number (ℝ)

Distinct278
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.16786
Minimum102
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:30.206340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile143.745
Q1192.375
median272.9
Q3350
95-th percentile480
Maximum540
Range438
Interquartile range (IQR)157.625

Descriptive statistics

Standard deviation104.50636
Coefficient of variation (CV)0.37168673
Kurtosis-0.52065228
Mean281.16786
Median Absolute Deviation (MAD)79.4
Skewness0.50948118
Sum289602.9
Variance10921.58
MonotonicityNot monotonic
2025-12-31T01:06:30.310945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362.620
 
1.9%
42520
 
1.9%
251.415
 
1.5%
31014
 
1.4%
44614
 
1.4%
33113
 
1.3%
47513
 
1.3%
25013
 
1.3%
34912
 
1.2%
38712
 
1.2%
Other values (268)884
85.8%
ValueCountFrequency (%)
1024
0.4%
108.34
0.4%
1164
0.4%
122.64
0.4%
1322
 
0.2%
1335
0.5%
133.11
 
0.1%
134.71
 
0.1%
1352
 
0.2%
135.72
 
0.2%
ValueCountFrequency (%)
5409
0.9%
531.35
0.5%
5281
 
0.1%
5257
0.7%
5222
 
0.2%
5202
 
0.2%
5162
 
0.2%
5051
 
0.1%
500.11
 
0.1%
50010
1.0%

Blast Furnace Slag
Real number (ℝ)

Zeros 

Distinct185
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.895825
Minimum0
Maximum359.4
Zeros471
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:30.417692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q3142.95
95-th percentile236
Maximum359.4
Range359.4
Interquartile range (IQR)142.95

Descriptive statistics

Standard deviation86.279342
Coefficient of variation (CV)1.1675807
Kurtosis-0.50817548
Mean73.895825
Median Absolute Deviation (MAD)22
Skewness0.8007169
Sum76112.7
Variance7444.1248
MonotonicityNot monotonic
2025-12-31T01:06:30.530263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0471
45.7%
18930
 
2.9%
106.320
 
1.9%
2414
 
1.4%
2012
 
1.2%
14511
 
1.1%
98.110
 
1.0%
1910
 
1.0%
268
 
0.8%
228
 
0.8%
Other values (175)436
42.3%
ValueCountFrequency (%)
0471
45.7%
114
 
0.4%
13.65
 
0.5%
155
 
0.5%
17.21
 
0.1%
17.51
 
0.1%
17.61
 
0.1%
1910
 
1.0%
2012
 
1.2%
228
 
0.8%
ValueCountFrequency (%)
359.42
 
0.2%
342.12
 
0.2%
316.12
 
0.2%
305.34
0.4%
290.22
 
0.2%
2884
0.4%
282.84
0.4%
272.82
 
0.2%
262.25
0.5%
2601
 
0.1%

Fly Ash
Real number (ℝ)

Zeros 

Distinct156
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.18835
Minimum0
Maximum200.1
Zeros566
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:30.649840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3118.3
95-th percentile167
Maximum200.1
Range200.1
Interquartile range (IQR)118.3

Descriptive statistics

Standard deviation63.997004
Coefficient of variation (CV)1.1810104
Kurtosis-1.3287464
Mean54.18835
Median Absolute Deviation (MAD)0
Skewness0.53735391
Sum55814
Variance4095.6165
MonotonicityNot monotonic
2025-12-31T01:06:30.779987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0566
55.0%
118.320
 
1.9%
14116
 
1.6%
24.515
 
1.5%
7914
 
1.4%
9413
 
1.3%
100.411
 
1.1%
125.210
 
1.0%
95.710
 
1.0%
98.810
 
1.0%
Other values (146)345
33.5%
ValueCountFrequency (%)
0566
55.0%
24.515
 
1.5%
591
 
0.1%
601
 
0.1%
711
 
0.1%
71.51
 
0.1%
75.61
 
0.1%
761
 
0.1%
772
 
0.2%
782
 
0.2%
ValueCountFrequency (%)
200.11
 
0.1%
2001
 
0.1%
1953
0.3%
194.91
 
0.1%
1941
 
0.1%
1931
 
0.1%
1901
 
0.1%
1871
 
0.1%
185.31
 
0.1%
1852
0.2%

Water
Real number (ℝ)

High correlation 

Distinct195
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.56728
Minimum121.8
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:30.919293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum121.8
5-th percentile146.1
Q1164.9
median185
Q3192
95-th percentile228
Maximum247
Range125.2
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation21.354219
Coefficient of variation (CV)0.1176105
Kurtosis0.12208167
Mean181.56728
Median Absolute Deviation (MAD)13
Skewness0.074628384
Sum187014.3
Variance456.00265
MonotonicityNot monotonic
2025-12-31T01:06:31.037497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192118
 
11.5%
22854
 
5.2%
185.746
 
4.5%
203.536
 
3.5%
18628
 
2.7%
164.920
 
1.9%
16220
 
1.9%
18515
 
1.5%
153.515
 
1.5%
20014
 
1.4%
Other values (185)664
64.5%
ValueCountFrequency (%)
121.85
0.5%
126.65
0.5%
1271
 
0.1%
127.31
 
0.1%
137.85
0.5%
1401
 
0.1%
140.85
0.5%
141.85
0.5%
1421
 
0.1%
143.35
0.5%
ValueCountFrequency (%)
2471
 
0.1%
246.91
 
0.1%
2371
 
0.1%
236.71
 
0.1%
22854
5.2%
221.41
 
0.1%
2212
 
0.2%
220.11
 
0.1%
2202
 
0.2%
219.71
 
0.1%

Superplasticizer
Real number (ℝ)

High correlation  Zeros 

Distinct111
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2046602
Minimum0
Maximum32.2
Zeros379
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:31.144767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.4
Q310.2
95-th percentile16.055
Maximum32.2
Range32.2
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation5.9738414
Coefficient of variation (CV)0.96279912
Kurtosis1.411269
Mean6.2046602
Median Absolute Deviation (MAD)5.3
Skewness0.90720257
Sum6390.8
Variance35.686781
MonotonicityNot monotonic
2025-12-31T01:06:31.253085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0379
36.8%
11.637
 
3.6%
827
 
2.6%
719
 
1.8%
617
 
1.7%
9.916
 
1.6%
8.916
 
1.6%
7.816
 
1.6%
916
 
1.6%
1015
 
1.5%
Other values (101)472
45.8%
ValueCountFrequency (%)
0379
36.8%
1.74
 
0.4%
1.91
 
0.1%
21
 
0.1%
2.21
 
0.1%
2.52
 
0.2%
36
 
0.6%
3.11
 
0.1%
3.43
 
0.3%
3.65
 
0.5%
ValueCountFrequency (%)
32.25
0.5%
28.25
0.5%
23.45
0.5%
22.11
 
0.1%
226
0.6%
20.81
 
0.1%
201
 
0.1%
191
 
0.1%
18.81
 
0.1%
18.65
0.5%

Coarse Aggregate
Real number (ℝ)

Distinct284
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972.91893
Minimum801
Maximum1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:31.563699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile842
Q1932
median968
Q31029.4
95-th percentile1104
Maximum1145
Range344
Interquartile range (IQR)97.4

Descriptive statistics

Standard deviation77.753954
Coefficient of variation (CV)0.079918225
Kurtosis-0.5990161
Mean972.91893
Median Absolute Deviation (MAD)46.3
Skewness-0.040219745
Sum1002106.5
Variance6045.6774
MonotonicityNot monotonic
2025-12-31T01:06:31.686478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93257
 
5.5%
852.145
 
4.4%
944.730
 
2.9%
96829
 
2.8%
112524
 
2.3%
104719
 
1.8%
96719
 
1.8%
97412
 
1.2%
94212
 
1.2%
93812
 
1.2%
Other values (274)771
74.9%
ValueCountFrequency (%)
8014
0.4%
801.11
 
0.1%
801.41
 
0.1%
8112
0.2%
8141
 
0.1%
814.11
 
0.1%
817.91
 
0.1%
8181
 
0.1%
8192
0.2%
819.21
 
0.1%
ValueCountFrequency (%)
11451
 
0.1%
1134.35
 
0.5%
11301
 
0.1%
112524
2.3%
1124.42
 
0.2%
11202
 
0.2%
11192
 
0.2%
1118.82
 
0.2%
11181
 
0.1%
11132
 
0.2%

Fine Aggregate
Real number (ℝ)

Distinct302
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.58049
Minimum594
Maximum992.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:31.815830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile613
Q1730.95
median779.5
Q3824
95-th percentile898.09
Maximum992.6
Range398.6
Interquartile range (IQR)93.05

Descriptive statistics

Standard deviation80.17598
Coefficient of variation (CV)0.10364271
Kurtosis-0.10217699
Mean773.58049
Median Absolute Deviation (MAD)45.5
Skewness-0.2530096
Sum796787.9
Variance6428.1878
MonotonicityNot monotonic
2025-12-31T01:06:31.939991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
755.830
 
2.9%
59430
 
2.9%
67023
 
2.2%
61322
 
2.1%
80116
 
1.6%
746.615
 
1.5%
887.115
 
1.5%
71214
 
1.4%
84514
 
1.4%
75012
 
1.2%
Other values (292)839
81.5%
ValueCountFrequency (%)
59430
2.9%
6055
 
0.5%
611.85
 
0.5%
6121
 
0.1%
61322
2.1%
613.22
 
0.2%
6141
 
0.1%
6232
 
0.2%
6305
 
0.5%
6314
 
0.4%
ValueCountFrequency (%)
992.65
0.5%
9454
0.4%
943.14
0.4%
9424
0.4%
925.75
0.5%
905.95
0.5%
903.85
0.5%
903.65
0.5%
901.85
0.5%
900.95
0.5%

Age
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.662136
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:32.038930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median28
Q356
95-th percentile180
Maximum365
Range364
Interquartile range (IQR)49

Descriptive statistics

Standard deviation63.169912
Coefficient of variation (CV)1.38342
Kurtosis12.168989
Mean45.662136
Median Absolute Deviation (MAD)21
Skewness3.2691774
Sum47032
Variance3990.4377
MonotonicityNot monotonic
2025-12-31T01:06:32.124958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
28425
41.3%
3134
 
13.0%
7126
 
12.2%
5691
 
8.8%
1462
 
6.0%
9054
 
5.2%
10052
 
5.0%
18026
 
2.5%
9122
 
2.1%
36514
 
1.4%
Other values (4)24
 
2.3%
ValueCountFrequency (%)
12
 
0.2%
3134
 
13.0%
7126
 
12.2%
1462
 
6.0%
28425
41.3%
5691
 
8.8%
9054
 
5.2%
9122
 
2.1%
10052
 
5.0%
1203
 
0.3%
ValueCountFrequency (%)
36514
 
1.4%
3606
 
0.6%
27013
 
1.3%
18026
 
2.5%
1203
 
0.3%
10052
 
5.0%
9122
 
2.1%
9054
 
5.2%
5691
 
8.8%
28425
41.3%

Strength
Real number (ℝ)

High correlation 

Distinct845
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.817961
Minimum2.33
Maximum82.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-12-31T01:06:32.225482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.33
5-th percentile10.961
Q123.71
median34.445
Q346.135
95-th percentile66.802
Maximum82.6
Range80.27
Interquartile range (IQR)22.425

Descriptive statistics

Standard deviation16.705742
Coefficient of variation (CV)0.46640684
Kurtosis-0.31372486
Mean35.817961
Median Absolute Deviation (MAD)10.93
Skewness0.41697729
Sum36892.5
Variance279.08181
MonotonicityNot monotonic
2025-12-31T01:06:32.349851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.46
 
0.6%
77.34
 
0.4%
79.34
 
0.4%
31.354
 
0.4%
71.34
 
0.4%
35.34
 
0.4%
23.524
 
0.4%
41.054
 
0.4%
44.283
 
0.3%
41.543
 
0.3%
Other values (835)990
96.1%
ValueCountFrequency (%)
2.331
0.1%
3.321
0.1%
4.571
0.1%
4.781
0.1%
4.831
0.1%
4.91
0.1%
6.271
0.1%
6.281
0.1%
6.471
0.1%
6.811
0.1%
ValueCountFrequency (%)
82.61
 
0.1%
81.751
 
0.1%
80.21
 
0.1%
79.991
 
0.1%
79.41
 
0.1%
79.34
0.4%
78.81
 
0.1%
77.34
0.4%
76.81
 
0.1%
76.241
 
0.1%

Interactions

2025-12-31T01:06:29.135315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:22.530180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.257885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.081956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.855944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:25.945275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.664952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:27.403644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:28.176460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:29.226878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:22.614676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.339319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.170357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.936061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.021594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.749787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:27.489050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:28.248922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:29.324163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:22.696567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.427955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.265138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:25.347067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.102280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.837409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:27.573453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:28.327971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:29.413217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:22.779372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.525896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.356521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:25.433274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.177099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.918892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-31T01:06:29.494585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:22.867866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.624365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.437835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:25.506835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.257853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-31T01:06:29.588409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:22.945686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.709961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.512864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:25.593187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-31T01:06:27.154402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-31T01:06:23.899284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-31T01:06:28.953467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:29.872020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.177454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:23.982871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:24.760259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:25.865096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:26.581730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:27.317060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:28.090572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-31T01:06:29.039982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-31T01:06:32.446842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBlast Furnace SlagCementCoarse AggregateFine AggregateFly AshStrengthSuperplasticizerWater
Age1.000-0.0180.005-0.045-0.0570.0030.596-0.0100.091
Blast Furnace Slag-0.0181.000-0.245-0.349-0.302-0.2540.1640.0980.053
Cement0.005-0.2451.000-0.145-0.174-0.4180.4780.038-0.094
Coarse Aggregate-0.045-0.349-0.1451.000-0.1000.058-0.184-0.199-0.218
Fine Aggregate-0.057-0.302-0.174-0.1001.0000.051-0.1800.168-0.346
Fly Ash0.003-0.254-0.4180.0580.0511.000-0.0780.454-0.283
Strength0.5960.1640.478-0.184-0.180-0.0781.0000.348-0.308
Superplasticizer-0.0100.0980.038-0.1990.1680.4540.3481.000-0.687
Water0.0910.053-0.094-0.218-0.346-0.283-0.308-0.6871.000

Missing values

2025-12-31T01:06:30.000321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-31T01:06:30.091479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeStrength
0540.00.00.0162.02.51040.0676.02879.99
1540.00.00.0162.02.51055.0676.02861.89
2332.5142.50.0228.00.0932.0594.027040.27
3332.5142.50.0228.00.0932.0594.036541.05
4198.6132.40.0192.00.0978.4825.536044.30
5266.0114.00.0228.00.0932.0670.09047.03
6380.095.00.0228.00.0932.0594.036543.70
7380.095.00.0228.00.0932.0594.02836.45
8266.0114.00.0228.00.0932.0670.02845.85
9475.00.00.0228.00.0932.0594.02839.29
CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeStrength
1020288.4121.00.0177.47.0907.9829.52842.14
1021298.20.0107.0209.711.1879.6744.22831.88
1022264.5111.086.5195.55.9832.6790.42841.54
1023159.8250.00.0168.412.21049.3688.22839.46
1024166.0259.70.0183.212.7858.8826.82837.92
1025276.4116.090.3179.68.9870.1768.32844.28
1026322.20.0115.6196.010.4817.9813.42831.18
1027148.5139.4108.6192.76.1892.4780.02823.70
1028159.1186.70.0175.611.3989.6788.92832.77
1029260.9100.578.3200.68.6864.5761.52832.40

Duplicate rows

Most frequently occurring

CementBlast Furnace SlagFly AshWaterSuperplasticizerCoarse AggregateFine AggregateAgeStrength# duplicates
1362.6189.00.0164.911.6944.7755.8335.304
3362.6189.00.0164.911.6944.7755.82871.304
4362.6189.00.0164.911.6944.7755.85677.304
5362.6189.00.0164.911.6944.7755.89179.304
2362.6189.00.0164.911.6944.7755.8755.903
6425.0106.30.0153.516.5852.1887.1333.403
7425.0106.30.0153.516.5852.1887.1749.203
8425.0106.30.0153.516.5852.1887.12860.293
9425.0106.30.0153.516.5852.1887.15664.303
10425.0106.30.0153.516.5852.1887.19165.203